变频环境下异步电机故障诊断方法

Fault Diagnosis Method of Induction Motor in Frequency Conversion Environment

针对变频环境下异步电机故障时定子电流信号非平稳的问题,提出一种互补集合经验模态分解(CEEMD)与卷积神经网络(CNN)结合的异步电机故障诊断方法。首先通过ANSYS对变频环境下电机建模获得仿真电流数据,利用CEEMD将电机定子电流信号分解为一系列本征模态函数(IMF);其次通过计算排列熵和样本熵,选取复杂程度小的IMF分量并计算其平均值来提取出故障特征;接着将特征数据集输入卷积神经网络(CNN)进行训练和验证;最后搭建实验平台收集电流信号,对信号进行滤波和CEEMD分解重构,放入CNN训练好的模型进行测试,识别率达95.56%。证明了该方法是一种可行的异步电机故障诊断方法,可实现对异步电机正常、转子断条和气隙偏心状态的准确识别。

Aiming at the problem that the stator current signal is not stationary when the induction motor fails under variable frequency environment,a fault diagnosis method for induction motor based on complementary set empirical mode decomposition(CEEMD)and convolutional neural network(CNN)was proposed.Firstly,the simulation current data were obtained by modeling the motor in frequency conversion environment with ANSYS,and the stator current signal was decomposed into a series of intrinsic mode functions(IMF)using CEEMD;secondly,by calculating permutation entropy and sample entropy,the IMF component with small complexity was selected and its average value was calculated to extract fault features;then,the feature data set was input into convolutional neural network(CNN)for training and verification;finally,the experimental platform was built to collect the current signal,and the signal was filtered and decomposed and reconstructed by CEEMD,which was put into the CNN trained model for testing,and the recognition rate reached 95.56%.It is proved that this method is a feasible fault diagnosis method for induction motor,which can accurately identify the normal state,broken rotor bar and air gap eccentricity of induction motor.